diff --git a/sdk/python/feast/pyspark/launchers/gcloud/dataproc.py b/sdk/python/feast/pyspark/launchers/gcloud/dataproc.py
index bd9cf11647..ea6ecdaae5 100644
--- a/sdk/python/feast/pyspark/launchers/gcloud/dataproc.py
+++ b/sdk/python/feast/pyspark/launchers/gcloud/dataproc.py
@@ -26,7 +26,12 @@
 
 class DataprocJobMixin:
     def __init__(
-        self, job: Job, refresh_fn: Callable[[], Job], cancel_fn: Callable[[], None]
+        self,
+        job: Job,
+        refresh_fn: Callable[[], Job],
+        cancel_fn: Callable[[], None],
+        project: str,
+        region: str,
     ):
         """
         Implementation of common methods for different types of SparkJob running on Dataproc cluster.
@@ -39,6 +44,8 @@ def __init__(
         self._job = job
         self._refresh_fn = refresh_fn
         self._cancel_fn = cancel_fn
+        self._project = project
+        self._region = region
 
     def get_id(self) -> str:
         """
@@ -134,7 +141,10 @@ def get_start_time(self):
         return self._job.status.state_start_time
 
     def get_log_uri(self) -> Optional[str]:
-        return self._job.driver_output_resource_uri
+        return (
+            f"https://console.cloud.google.com/dataproc/jobs/{self.get_id()}"
+            f"?region={self._region}&project={self._project}"
+        )
 
 
 class DataprocRetrievalJob(DataprocJobMixin, RetrievalJob):
@@ -147,6 +157,8 @@ def __init__(
         job: Job,
         refresh_fn: Callable[[], Job],
         cancel_fn: Callable[[], None],
+        project: str,
+        region: str,
         output_file_uri: str,
     ):
         """
@@ -155,7 +167,7 @@ def __init__(
         Args:
             output_file_uri (str): Uri to the historical feature retrieval job output file.
         """
-        super().__init__(job, refresh_fn, cancel_fn)
+        super().__init__(job, refresh_fn, cancel_fn, project, region)
         self._output_file_uri = output_file_uri
 
     def get_output_file_uri(self, timeout_sec=None, block=True):
@@ -187,9 +199,11 @@ def __init__(
         job: Job,
         refresh_fn: Callable[[], Job],
         cancel_fn: Callable[[], None],
+        project: str,
+        region: str,
         job_hash: str,
     ) -> None:
-        super().__init__(job, refresh_fn, cancel_fn)
+        super().__init__(job, refresh_fn, cancel_fn, project, region)
         self._job_hash = job_hash
 
     def get_hash(self) -> str:
@@ -379,21 +393,39 @@ def historical_feature_retrieval(
             job_params, {"dev.feast.outputuri": job_params.get_destination_path()}
         )
         return DataprocRetrievalJob(
-            job, refresh_fn, cancel_fn, job_params.get_destination_path()
+            job=job,
+            refresh_fn=refresh_fn,
+            cancel_fn=cancel_fn,
+            project=self.project_id,
+            region=self.region,
+            output_file_uri=job_params.get_destination_path(),
         )
 
     def offline_to_online_ingestion(
         self, ingestion_job_params: BatchIngestionJobParameters
     ) -> BatchIngestionJob:
         job, refresh_fn, cancel_fn = self.dataproc_submit(ingestion_job_params, {})
-        return DataprocBatchIngestionJob(job, refresh_fn, cancel_fn)
+        return DataprocBatchIngestionJob(
+            job=job,
+            refresh_fn=refresh_fn,
+            cancel_fn=cancel_fn,
+            project=self.project_id,
+            region=self.region,
+        )
 
     def start_stream_to_online_ingestion(
         self, ingestion_job_params: StreamIngestionJobParameters
     ) -> StreamIngestionJob:
         job, refresh_fn, cancel_fn = self.dataproc_submit(ingestion_job_params, {})
         job_hash = ingestion_job_params.get_job_hash()
-        return DataprocStreamingIngestionJob(job, refresh_fn, cancel_fn, job_hash)
+        return DataprocStreamingIngestionJob(
+            job=job,
+            refresh_fn=refresh_fn,
+            cancel_fn=cancel_fn,
+            project=self.project_id,
+            region=self.region,
+            job_hash=job_hash,
+        )
 
     def get_job_by_id(self, job_id: str) -> SparkJob:
         job = self.job_client.get_job(
@@ -414,14 +446,34 @@ def _dataproc_job_to_spark_job(self, job: Job) -> SparkJob:
 
         if job_type == SparkJobType.HISTORICAL_RETRIEVAL.name.lower():
             output_path = job.pyspark_job.properties.get("dev.feast.outputuri", "")
-            return DataprocRetrievalJob(job, refresh_fn, cancel_fn, output_path)
+            return DataprocRetrievalJob(
+                job=job,
+                refresh_fn=refresh_fn,
+                cancel_fn=cancel_fn,
+                project=self.project_id,
+                region=self.region,
+                output_file_uri=output_path,
+            )
 
         if job_type == SparkJobType.BATCH_INGESTION.name.lower():
-            return DataprocBatchIngestionJob(job, refresh_fn, cancel_fn)
+            return DataprocBatchIngestionJob(
+                job=job,
+                refresh_fn=refresh_fn,
+                cancel_fn=cancel_fn,
+                project=self.project_id,
+                region=self.region,
+            )
 
         if job_type == SparkJobType.STREAM_INGESTION.name.lower():
             job_hash = job.labels[self.JOB_HASH_LABEL_KEY]
-            return DataprocStreamingIngestionJob(job, refresh_fn, cancel_fn, job_hash)
+            return DataprocStreamingIngestionJob(
+                job=job,
+                refresh_fn=refresh_fn,
+                cancel_fn=cancel_fn,
+                project=self.project_id,
+                region=self.region,
+                job_hash=job_hash,
+            )
 
         raise ValueError(f"Unrecognized job type: {job_type}")